Computer technology as brought about a variety of graphics and image processing systems, from graphics animation systems to pattern recognition systems (such as neural networks). Important to such systems is the accuracy of generated (output) images and in particular image sequences.
In general, graphic animation today is typically based on the three steps of (i) three dimensional modeling of surfaces of an object of interest, (ii) physically based simulation of movements, and (iii) rendering or computer illumination of three dimensional images from calculated surfaces. The step of three dimensional modeling is typically based on a three dimensional description including x, y, z axis specifications and surface specifications. The resulting 3-D model is considered to be a physically based model. To that end, every prospective view is computable. Movement such as rotation of the whole model or portions thereof, and illumination is then accomplished through computer aided design (CAD) systems and the like. While this 3-D modeling and physical simulation approach to graphic animation is clearly fundamentally correct and potentially powerful, current results are still far from obtaining general purpose, realistic image sequences.
Before the use of three dimensional, physically based models of objects for graphics animation, two dimensional images were used. In a two dimensional image of an object only a single perspective view is provided, i.e., is computable. Basically a series of 2-D images of an object in respective poses provides the illusion of whole object or object parts movement, and hence graphic animation. This 2-D serial image approach to graphic animation is cumbersome and often requires repetition in drawing/providing portions of views from one view to succeeding views, and is thus riddled with many inefficiencies. The 3-D model based approach with computer support was developed with the advent of computer technology to improve on and preclude the inefficiencies of the 2-D image graphic animation approach.
In addition to computer systems for generating graphics animation, there are computer systems for categorizing or recognizing patterns, or more generally mapping patterns, in 3-D or 2-D views/images, occluded images and/or noisy images. These computer systems are sometimes referred to as neural networks. Typically a neural network is predefined or trained to produce a target output for a certain input. Pairs of example mappings (certain input-target output) collectively called the "training set" are presented to the neural network during the predefining stage called learning. During learning, internal parameters of the neural network are made to converge to respective values that produce the desired mappings for any subsequent given input. Thereafter, the neural network operates on subject input patterns and provides an output pattern according to the learned mapping.